havaintojoukko

Havaintojoukko is a collection of measurements or observations gathered from experiments, surveys, or sensors. In statistics and data science, it is commonly referred to as a dataset. The set consists of individual observations, each described by variables or features, and it may include a target variable for supervised tasks.

Observations are typically organized as rows in a table, with variables as columns. The number of variables determines the dimensionality of the data, while the number of observations determines the size of the dataset. Data can be structured (tabular) or unstructured, but in many applications a havaintojoukko is represented in tabular form as an n-by-d matrix, sometimes accompanied by a corresponding target vector or matrix for predictive modeling.

Ahavaintojoukko is usually drawn from a population, and a subset of it is used for analysis or model training. The assumption that observations are independent and identically distributed (iid) is common, but real-world data often deviate from this ideal due to sampling bias, time dependence, or missing values.

Data quality aspects are central to havaintojoukko: missing values, measurement errors, outliers, inconsistencies, and noise can affect analyses. Preprocessing steps such as imputation, normalization, and encoding of categorical variables are frequently performed before statistical analysis or machine learning.

Uses include describing data, performing statistical inference, training and evaluating predictive models, and performing hypothesis testing. In practice, a havaintojoukko may be split into training, validation, and test sets to assess model performance, or inspected to understand distributions and relationships among variables.

Example: the Iris data set is a havaintojoukko consisting of measurements of sepal length and width, petal length and width for three species, used for both descriptive and predictive tasks.